skill-autoctx-bootstrap

name	skill-autoctx-bootstrap
description	Guides the agent to bootstrap an initial context set (templates & facets) by deducing key information from the database schema and generating a ContextSet file.

Auto Context Generation - Bootstrap Workflow

This skill guides the process of bootstrapping an initial ContextSet (baseline context) from the target database schema.

Input

Before beginning the workflow, you explicitly require:

• An active tools.yaml configuration (located in autoctx/) with database schema fetching tools configured (e.g., <source>-list-schemas).
• Target database schemas to act upon.

Workflow

Follow these steps exactly in order:

1. Condition Check & Schema Retrieval:

   • You must explicitly ask the user for a descriptive name for this tuning experiment (e.g., sales_db_tuning). A new dedicated subfolder will be created inside the autoctx/experiments/ directory using this name to hold the entire tuning lifecycle and prevent any surprises. Do not proceed until you have their confirmation.
   • Use the available Toolbox MCP tools configured in the active autoctx/tools.yaml to fetch the schemas for the target database.
   • Present the retrieved schema summary structurally and cleanly to the user. Ask the user if they want to filter or focus on specific schemas or tables.
   • Source Enrichment: Prompt the user for any existing Design Docs or Application Code (e.g., ORM models, SQL queries) they wish to provide to enrich the context generation. Wait for the user's response before proceeding.

2. Deduce Key Info (Core Execution):

   • Perform a deep analysis of the retrieved schema and any provided documentation or code to identify important concepts, relationships, and likely query patterns.
   • Collect Candidates: Identify representative natural language queries with their corresponding SQL, common filter conditions or business rules, and columns that require specialized value searching (e.g., names needing fuzzy match, descriptions needing semantic search).
   • Review Check: Briefly display these candidates to the user for approval or modifications before proceeding.

3. Context Generation (Core Execution):

   • Invoke the context-generation-guide skill to produce the context (Templates, Facets, and Value Searches).
   • Provide the deduced candidates collected in Step 2 as input to that skill.
   • That skill will handle phrase extraction, parameterization, and constructing the final valid JSON structure according to dialect best practices for all context types.
   • Once generated, use the mutate_context_set MCP tool to save the context items to bootstrap_context.json inside the approved experiment folder. Since this is a new file, construct a list of "operation": "add" mutations for each generated item (Template, Facet, Value Search) and pass them to the tool.

Output

Upon successful completion, the workspace must contain:

• A generated .json file (bootstrap_context.json) representing the baseline ContextSet, stored successfully at the requested output_file_path.

Upload Advice & Next Steps

Conclude by providing a succinct summary to the user:

1. Summarize Results:
   • Confirm that the bootstrap context file has been successfully generated and saved.
   • Mention the final file path.
2. Upload Instructions:
   • Read Database Details: Read autoctx/tools.yaml to fetch the specific project, location, and instance/cluster details for the active database.
   • Generate URL: Call the generate_upload_url tool passing the extracted values to provide the direct console link to the user.
   • Present the local file path to bootstrap_context.json and the generated console link together in a single clear message.
3. Instruct Next Step Evaluation:
   • Instruct the user to upload the file to Database Studio and then run evaluation using the evaluating workflow on this new ContextSet to establish a baseline.